Disclosure: I work on Hivemind, an open-source skills layer for coding agents. Sharing an update because the problem behind it is one a lot of people here run into. Hivemind captures what your agent does, turns repeated patterns into reusable skills, and shares them across whatever agents your team runs (Claude Code, Codex, Cursor, Hermes, pi). The gap was that skills just accumulated. A growing pile of skills is not the same as skills that get better. So we implemented SkillOpt, a text-space optimizer out of Microsoft and collaborators (arXiv 2605.23904). Instead of just storing a skill, Hivemind now scores the sessions where that skill actually got used, keeps the edits that help, and drops the ones that don’t. The skill gets sharper over time. The model itself never changes, so there’s no fine-tuning and no added cost at inference. The thing people ask first: this isn’t memory. Memory recalls what happened. This changes what the agent is good at. In the paper, the approach added +19.1 accuracy points inside Claude Code and won or tied on all 52 setups tested. It’s Apache 2.0 and self-hostable, so traces and skills can stay in your own infra. Free tier. OSS Repo: github.com/activeloopai/hivemind Paper: arxiv.org/pdf/2605.23904 Happy to go deep on the optimizer or the cross-agent part. submitted by /u/davidbun
Originally posted by u/davidbun on r/ClaudeCode